AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies whether a carefully domain-adapted retrieval-augmented generation pipeline closes the gap between compact and compact model quality in financial institutions under dense, frequently amended rulebooks.
As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has become a critical practice. However, the impact of this anonymization on model utility is not well understood. This paper presents a systematic empirical study of the trade-...
Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely based on zero-shot task accuracy, which depends solely on argmax predictions and is insensitive to changes in the underlying predictive distr...
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